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459 lines
143 KiB
459 lines
143 KiB
2 years ago
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{
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"cells": [
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"___\n",
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"\n",
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"<a href='http://www.pieriandata.com'> <img src='../Pierian_Data_Logo.png' /></a>\n",
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"___\n",
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"# Matplotlib Exercises \n",
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"Welcome to the exercises for reviewing matplotlib! Take your time with these, Matplotlib can be tricky to understand at first. These are relatively simple plots, but they can be hard if this is your first time with matplotlib, feel free to reference the solutions as you go along.\n",
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"\n",
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"Also don't worry if you find the matplotlib syntax frustrating, we actually won't be using it that often throughout the course, we will switch to using seaborn and pandas built-in visualization capabilities. But, those are built-off of matplotlib, which is why it is still important to get exposure to it!\n",
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"\n",
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"**NOTE: ALL THE COMMANDS FOR PLOTTING A FIGURE SHOULD ALL GO IN THE SAME CELL. SEPARATING THEM OUT INTO MULTIPLE CELLS MAY CAUSE NOTHING TO SHOW UP.**\n",
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"\n",
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"# Exercises\n",
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"\n",
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"**We will focus on two commons tasks, plotting a known relationship from an equation and plotting raw data points.**\n",
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"\n",
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"Follow the instructions to complete the tasks to recreate the plots using this data:\n",
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"\n",
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"----\n",
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"----\n",
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"\n",
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"### Task One: Creating data from an equation\n",
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"\n",
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"It is important to be able to directly translate a real equation into a plot. Your first task actually is pure numpy, then we will explore how to plot it out with Matplotlib. The [world famous equation](https://en.wikipedia.org/wiki/Mass%E2%80%93energy_equivalence) from Einstein:\n",
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"\n",
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"$$E=mc^2$$\n",
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"\n",
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"\n",
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"Use your knowledge of Numpy to create two arrays: E and m , where **m** is simply 11 evenly spaced values representing 0 grams to 10 grams. E should be the equivalent energy for the mass. You will need to figure out what to provide for **c** for the units m/s, a quick google search will easily give you the answer (we'll use the close approximation in our solutions).\n",
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"\n",
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"**NOTE: If this confuses you, then hop over to the solutions video for a guided walkthrough.**"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"# CODE HERE"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 11,
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"The array m should look like this: \n",
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"\n",
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"[ 0. 1. 2. 3. 4. 5. 6. 7. 8. 9. 10.]\n"
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]
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}
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],
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"source": []
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},
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{
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"cell_type": "code",
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"execution_count": 12,
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"metadata": {},
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"outputs": [],
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"source": [
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"# CODE HERE"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 15,
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"The array E should look like this: \n",
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"\n",
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" [0.0e+00 9.0e+16 1.8e+17 2.7e+17 3.6e+17 4.5e+17 5.4e+17 6.3e+17 7.2e+17\n",
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" 8.1e+17 9.0e+17]\n"
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]
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}
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],
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"source": []
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"### Part Two: Plotting E=mc^2\n",
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"\n",
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"Now that we have the arrays E and m, we can plot this to see the relationship between Energy and Mass. \n",
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"\n",
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"**TASK: Import what you need from Matplotlib to plot out graphs:**"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 16,
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"metadata": {},
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"outputs": [],
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"source": []
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"**TASK: Recreate the plot shown below which maps out E=mc^2 using the arrays we created in the previous task. Note the labels, titles, color, and axis limits. You don't need to match perfectly, but you should attempt to re-create each major component.**"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 22,
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"metadata": {},
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"outputs": [],
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"source": [
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"# CODE HERE"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 23,
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"metadata": {},
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"outputs": [],
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"source": [
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"# DON\"T RUN THE CELL BELOW< THAT WILL ERASE THE PLOT!"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 24,
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"metadata": {},
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"outputs": [
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{
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"data": {
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"image/png": "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"text/plain": [
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"<Figure size 432x288 with 1 Axes>"
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]
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},
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"metadata": {
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"needs_background": "light"
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},
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"output_type": "display_data"
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}
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],
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"source": []
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"### Part Three (BONUS)\n",
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"\n",
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"**Can you figure out how to plot this on a logarthimic scale on the y axis? Place a grid along the y axis ticks as well. We didn't show this in the videos, but you should be able to figure this out by referencing Google, StackOverflow, Matplotlib Docs, or even our \"Additional Matplotlib Commands\" notebook. The plot we show here only required two more lines of code for the changes.**"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 28,
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"metadata": {},
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"outputs": [],
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"source": [
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"# CODE HERE"
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]
|
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},
|
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{
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"cell_type": "code",
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"execution_count": 29,
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"metadata": {},
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"outputs": [],
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"source": [
|
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|
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"# DONT RUN THE CELL BELOW! THAT WILL ERASE THE PLOT!"
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|
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]
|
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},
|
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{
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"cell_type": "code",
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"execution_count": 27,
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|
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"metadata": {},
|
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|
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"outputs": [
|
||
|
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{
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||
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"data": {
|
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|
|
"image/png": "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
|
||
|
|
"text/plain": [
|
||
|
|
"<Figure size 432x288 with 1 Axes>"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
"metadata": {
|
||
|
|
"needs_background": "light"
|
||
|
|
},
|
||
|
|
"output_type": "display_data"
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"source": []
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "markdown",
|
||
|
|
"metadata": {
|
||
|
|
"collapsed": true
|
||
|
|
},
|
||
|
|
"source": [
|
||
|
|
"---\n",
|
||
|
|
"---\n",
|
||
|
|
"\n",
|
||
|
|
"## Task Two: Creating plots from data points\n",
|
||
|
|
"\n",
|
||
|
|
"In finance, the yield curve is a curve showing several yields to maturity or interest rates across different contract lengths (2 month, 2 year, 20 year, etc. ...) for a similar debt contract. The curve shows the relation between the (level of the) interest rate (or cost of borrowing) and the time to maturity, known as the \"term\", of the debt for a given borrower in a given currency.\n",
|
||
|
|
"\n",
|
||
|
|
"The U.S. dollar interest rates paid on U.S. Treasury securities for various maturities are closely watched by many traders, and are commonly plotted on a graph such as the one on the right, which is informally called \"the yield curve\".\n",
|
||
|
|
"\n",
|
||
|
|
"**For this exercise, we will give you the data for the yield curves at two separate points in time. Then we will ask you to create some plots from this data.**\n",
|
||
|
|
"\n",
|
||
|
|
"## Part One: Yield Curve Data\n",
|
||
|
|
"\n",
|
||
|
|
"**We've obtained some yeild curve data for you from the [US Treasury Dept.](https://www.treasury.gov/resource-center/data-chart-center/interest-rates/pages/textview.aspx?data=yield). The data shows the interest paid for a US Treasury bond for a certain contract length. The labels list shows the corresponding contract length per index position.**\n",
|
||
|
|
"\n",
|
||
|
|
"**TASK: Run the cell below to create the lists for plotting.**"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 31,
|
||
|
|
"metadata": {},
|
||
|
|
"outputs": [],
|
||
|
|
"source": [
|
||
|
|
"labels = ['1 Mo','3 Mo','6 Mo','1 Yr','2 Yr','3 Yr','5 Yr','7 Yr','10 Yr','20 Yr','30 Yr']\n",
|
||
|
|
"\n",
|
||
|
|
"july16_2007 =[4.75,4.98,5.08,5.01,4.89,4.89,4.95,4.99,5.05,5.21,5.14]\n",
|
||
|
|
"july16_2020 = [0.12,0.11,0.13,0.14,0.16,0.17,0.28,0.46,0.62,1.09,1.31]"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "markdown",
|
||
|
|
"metadata": {},
|
||
|
|
"source": [
|
||
|
|
"**TASK: Figure out how to plot both curves on the same Figure. Add a legend to show which curve corresponds to a certain year.**"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 37,
|
||
|
|
"metadata": {},
|
||
|
|
"outputs": [],
|
||
|
|
"source": [
|
||
|
|
"# CODE HERE"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 38,
|
||
|
|
"metadata": {},
|
||
|
|
"outputs": [],
|
||
|
|
"source": [
|
||
|
|
"# DONT RUN THE CELL BELOW! IT WILL ERASE THE PLOT!"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 36,
|
||
|
|
"metadata": {},
|
||
|
|
"outputs": [
|
||
|
|
{
|
||
|
|
"data": {
|
||
|
|
"image/png": "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
|
||
|
|
"text/plain": [
|
||
|
|
"<Figure size 432x288 with 1 Axes>"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
"metadata": {
|
||
|
|
"needs_background": "light"
|
||
|
|
},
|
||
|
|
"output_type": "display_data"
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"source": []
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "markdown",
|
||
|
|
"metadata": {},
|
||
|
|
"source": [
|
||
|
|
"**TASK: The legend in the plot above looks a little strange in the middle of the curves. While it is not blocking anything, it would be nicer if it were *outside* the plot. Figure out how to move the legend outside the main Figure plot.**"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 44,
|
||
|
|
"metadata": {},
|
||
|
|
"outputs": [],
|
||
|
|
"source": [
|
||
|
|
"# CODE HERE"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 45,
|
||
|
|
"metadata": {},
|
||
|
|
"outputs": [],
|
||
|
|
"source": [
|
||
|
|
"# DONT RUN THE CELL BELOW! IT WILL ERASE THE PLOT!"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 47,
|
||
|
|
"metadata": {},
|
||
|
|
"outputs": [
|
||
|
|
{
|
||
|
|
"data": {
|
||
|
|
"image/png": "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
|
||
|
|
"text/plain": [
|
||
|
|
"<Figure size 432x288 with 1 Axes>"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
"metadata": {
|
||
|
|
"needs_background": "light"
|
||
|
|
},
|
||
|
|
"output_type": "display_data"
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"source": []
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "markdown",
|
||
|
|
"metadata": {},
|
||
|
|
"source": [
|
||
|
|
"**TASK: While the plot above clearly shows how rates fell from 2007 to 2020, putting these on the same plot makes it difficult to discern the rate differences within the same year. Use .suplots() to create the plot figure below, which shows each year's yield curve.**"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": null,
|
||
|
|
"metadata": {},
|
||
|
|
"outputs": [],
|
||
|
|
"source": [
|
||
|
|
"# CODE HERE"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 51,
|
||
|
|
"metadata": {},
|
||
|
|
"outputs": [],
|
||
|
|
"source": [
|
||
|
|
"# DONT RUN THE CELL BELOW! IT WILL ERASE THE PLOT!"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 52,
|
||
|
|
"metadata": {},
|
||
|
|
"outputs": [
|
||
|
|
{
|
||
|
|
"data": {
|
||
|
|
"image/png": "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
|
||
|
|
"text/plain": [
|
||
|
|
"<Figure size 864x576 with 2 Axes>"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
"metadata": {
|
||
|
|
"needs_background": "light"
|
||
|
|
},
|
||
|
|
"output_type": "display_data"
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"source": []
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "markdown",
|
||
|
|
"metadata": {},
|
||
|
|
"source": [
|
||
|
|
"**BONUS CHALLENGE TASK: Try to recreate the plot below that uses twin axes. While this plot may actually be more confusing than helpful, its a good exercise in Matplotlib control.**"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 62,
|
||
|
|
"metadata": {},
|
||
|
|
"outputs": [],
|
||
|
|
"source": [
|
||
|
|
"# CODE HERE"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 63,
|
||
|
|
"metadata": {},
|
||
|
|
"outputs": [],
|
||
|
|
"source": [
|
||
|
|
"# DONT RUN THE CELL BELOW! IT ERASES THE PLOT!"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 73,
|
||
|
|
"metadata": {},
|
||
|
|
"outputs": [
|
||
|
|
{
|
||
|
|
"data": {
|
||
|
|
"text/plain": [
|
||
|
|
"(array([0. , 0.2, 0.4, 0.6, 0.8, 1. , 1.2, 1.4]),\n",
|
||
|
|
" <a list of 8 Text yticklabel objects>)"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
"execution_count": 73,
|
||
|
|
"metadata": {},
|
||
|
|
"output_type": "execute_result"
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"data": {
|
||
|
|
"image/png": "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
|
||
|
|
"text/plain": [
|
||
|
|
"<Figure size 864x576 with 2 Axes>"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
"metadata": {
|
||
|
|
"needs_background": "light"
|
||
|
|
},
|
||
|
|
"output_type": "display_data"
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"source": []
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "markdown",
|
||
|
|
"metadata": {},
|
||
|
|
"source": [
|
||
|
|
"-----"
|
||
|
|
]
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"metadata": {
|
||
|
|
"kernelspec": {
|
||
|
|
"display_name": "Python 3",
|
||
|
|
"language": "python",
|
||
|
|
"name": "python3"
|
||
|
|
},
|
||
|
|
"language_info": {
|
||
|
|
"codemirror_mode": {
|
||
|
|
"name": "ipython",
|
||
|
|
"version": 3
|
||
|
|
},
|
||
|
|
"file_extension": ".py",
|
||
|
|
"mimetype": "text/x-python",
|
||
|
|
"name": "python",
|
||
|
|
"nbconvert_exporter": "python",
|
||
|
|
"pygments_lexer": "ipython3",
|
||
|
|
"version": "3.7.4"
|
||
|
|
}
|
||
|
|
},
|
||
|
|
"nbformat": 4,
|
||
|
|
"nbformat_minor": 1
|
||
|
|
}
|